The subject of federated filtering, and specifically federated navigation with multiple IMUs, has received considerable attention over the past three decades. The paper by M. S. Mahmoud and H. M. Khalid, entitled “Distributed Kalman filtering: a bibliographic review”, in Control Theory & Applications, IET, Volume 7, Issue 4, 2013, Digital Object Identifier 10.1049/iet-cta.2012.0732, contains a broad systematic review of distributed Kalman filtering, not necessarily in the context of navigation. The seminal works of Carlson, “Information-Sharing Approach to Federated Kalman Filtering”, Proceedings of the 1988 IEEE National Aerospace and Engineering Conference (NAECON 88), p. 1581, Dayton, Ohio, 1988, “Federated Filter for Fault-Tolerant Integrated Navigation Systems”, IEEE Position, Location, and Navigation Symposium, pp. 110-119, Orlando, Fla., 1988, “Federated square root filter for decentralized parallel processes”, IEEE Trans, on Aerospace and Electronic Systems, Vol. 26(3), pp. 517-525, 1990, “Federated filter for computer efficient near-optimal GPS integration”, IEEE Trans, on Aerospace and Electronic Systems, pp. 306-314, 1996, and “Federated Filter for Distributed Navigation and Tracking Applications”, ION 58th AM, pp. 340-353, 2002, have introduced decentralized/federated filtering ideas and techniques into the realm of navigation. These navigation approaches were thoroughly investigated at the USAF institute of technology, P. J. Lawrence, “Comparison of a Distributed Kalman Filter Versus a Centralized Kalman Filter with Fault Detection Considerations”, AFIT MS Thesis, Wright-Patterson AFB, AFIT/GE/ENG/93S-06, 1993; and S. J. Delory, “Design and Analysis of a Navigation System Using The Federated Filter”, AFIT MS Thesis, Wright-Patterson AFB, AFIT/GSO/IENG/95D-02, 1995. Some more recent works on this subject are exemplified in A. Edelmayer and M. Miranda, “Federated filtering for fault tolerant estimation and sensor redundancy management in coupled dynamics distributed systems”, Proceedings of the 15th Med. Conf. on Control & Automation, Athens, July 2007; I. Rapoport, A. Brandes and H. Kraus, “Optimal Fusion of Multiple Sensors with a Common External Update”, Proceedings of the 53rd Israel Annual Conference on Aerospace Sciences, Tel-Aviv & Haifa, Israel, March 2013; and T.-G. Lee, “Centralized Kalman Filter with Adaptive Measurement Fusion: its Application to a GPS/SDINS Integration System with an Additional Sensor”, International Journal of Control, Automation, and Systems, Vol. 1(4), pp. 444-452, December 2003. P. G. Savage, “Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms”, Journal of Guidance, Control, and Dynamics, Vol. 21(1), pp. 19-28 January-February 1998; P. G. Savage, “Strapdown Inertial Navigation Integration Algorithm Design Part 2: Velocity and Position Algorithms”, Journal of Guidance, Control, and Dynamics, Vol. 21(2), pp. 208-318, March-April 1998; P. G. Savage, “A Unified Mathematical Framework for Strapdown Algorithm Design”, Journal of Guidance, Control, and Dynamics, Vol. 29(2), pp. 237-249, March-April 2006; P. G. Savage, “Strapdown Analytics”, chap. 4.6, 6, 15.2, Strapdwon Associates, 2000; and D. H. Titterton and J. L. Weston, “Strapdown Inertial Navigation Technology”, IEE Radar, Sonar, Navigation and Avionics Series 5, 1997, are encompassing papers and books on inertial navigation. R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems”, Transactions ASME 82D, pp. 33-45, 1960; P. S. Maybeck, “Stochastic Models, Estimation and Control”, Vol. 1, Navtech Book & Software Store, 1994; and Y. Bar-Shalom, X.-Rong Li and T. Kirubarajan, “Estimation with Applications To Tracking and Navigation”, John Wiley & Sons, 2001 are the cornerstone paper on Kalman filtering and two well-known practical books on the subject. The basic kinematic and dynamic relationships between points within a rigid-body appear in numerous sources, e.g. B. Etkin, “Dynamics of Atmospheric Flight”, John Wiley & Sons, 1972.